Abstract
In real applications, labeled instances are often deficient which makes the classification problem on the target task difficult. To solve this problem, transfer learning techniques are introduced to make use of existing knowledge from the source data sets to the target data set. However, due to the discrepancy of distributions between tasks, directly transferring knowledge will possibly lead to degenerated performance which is also called negative trasnfer. In this paper, we adopted the Gaussian process to alleviate this problem by directly evaluating the distribution differences, with the parameter-free Minimum Description Length Principle (MDLP) for encoding. The proposed method inherits the good property of solid theoretical foundation as well as noise-tolerance. Extensive experiments results show the effectiveness of our method.
This work was supported by Humanity and Social Science Youth foundation of Ministry of Education of China (No. 13YJC630126), the Fundamental Research Funds for the Central Universities (No.WK0110000032), the NSFC (No.71171184/71201059/71201151), the Funds for Creative Research Group of China (No. 70821001) and the NSFC major program (No.71090401/71090400).
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Argyriou, A., Maurer, A., Pontil, M.: An algorithm for transfer learning in a heterogeneous environment. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 71–85. Springer, Heidelberg (2008)
Bakker, B., Heskes, T.: Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research 4, 83–99 (2003)
Cao, B., Pan, S.J., Yang, Q.: Adaptive Transfer Learning. In: AAAI 2010 (2010)
Wallace, C., Patrick, J.: Coding Decision Trees. Machine Learning 11(1), 7–22 (1993)
Shao, H., Tong, B., Suzuki, E.: Compact Coding for Hyperplane Classifiers in Heterogeneous Environment. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 207–222. Springer, Heidelberg (2011)
Shao, H., Tong, B., Suzuki, E.: Extended MDL Principle for Feature-based Inductive Transfer Learning. Knowledge and Information Systems 35(2), 365–389 (2013)
Shao, H., Suzuki, E.: Feature-based Inductive Transfer Learning through Minimum Encoding. In: SDM 2011, pp. 259–270 (2011)
Shao, H., Tong, B., Suzuki, E.: Query by Committee in a Heterogeneous Environment. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 186–198. Springer, Heidelberg (2012)
Quinlan, J.R., Rivest, R.L.: Inferring Decision Trees Using the Minimum Description Length Principle. Information and Computation 80(3), 227–248 (1989)
Rosenstein, M.T., Marx, Z., Kaelbling, L.P.: To Transfer or Not To Transfer. In: NIPS 2005 Workshop on Transfer Learning (2005)
Grünwald, P.D.: The Minimum Description Length Principle. MIT Press, Cambridge (2007)
Ben-David, S., Schuller, R.: Exploiting task relatedness for multiple task learning. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 567–580. Springer, Heidelberg (2003)
Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2009)
Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for Transfer Learning. In: ICML 2007, pp. 193–200 (2007)
Shi, X., Fan, W., Ren, J.: Actively Transfer Domain Knowledge. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 342–357. Springer, Heidelberg (2008)
Shi, Y., Lan, Z., Liu, W., Bi, W.: Extended Semi-supervised Learning Methods for Inductive Transfer Learning. In: ICDM 2009, pp. 483–492 (2009)
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Shao, H., Xu, R., Tao, F. (2013). Gaussian Process for Transfer Learning through Minimum Encoding. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_47
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DOI: https://doi.org/10.1007/978-3-642-41278-3_47
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